I saw lots of articles esposing this. Chest-beating congratulatory thumping. This is actually a piece of shit, absolutely nothing that can be measured and nothing that says that tPA is a failure or even any mention of stopping the neuronal cascade of death. Gawd the stupidity.
http://www.theheart.org/article/1575265.do?utm_medium=email&utm_source=20130831_ESC_EN&utm_campaign=newsletter
Development and Validation of the Post-stroke Depression Prediction Scale
- Janneke M. de Man-van Ginkel, RN, PhD;
- Thóra B. Hafsteinsdóttir, RN, PhD;
- Eline Lindeman, MD, PhD;
- Roelof G.A. Ettema, RN, MSc;
- Diederick E. Grobbee, MD, PhD, FESC;
- Marieke J. Schuurmans, RN, PhD
+ Author Affiliations
- Correspondence to Janneke M. de Man-van Ginkel, RN, PhD, Division Neurosciences, UMC Utrecht, W01.121, Heidelberglaan 100, 3584 XC Utrecht, The Netherlands. E-mail J.M.deMan@umcutrecht.nl
Abstract
Background and Purpose—The
timely detection of post-stroke depression is complicated by a
decreasing length of hospital stay. Therefore, the Post-stroke
Depression Prediction Scale was developed and
validated. The Post-stroke Depression Prediction Scale is a clinical
prediction
model for the early identification of stroke
patients at increased risk for post-stroke depression.
Methods—The study
included 410 consecutive stroke patients who were able to communicate
adequately. Predictors were collected within
the first week after stroke. Between 6 to 8
weeks after stroke, major depressive disorder was diagnosed using the
Composite
International Diagnostic Interview.
Multivariable logistic regression models were fitted. A
bootstrap-backward selection process
resulted in a reduced model. Performance of
the model was expressed by discrimination, calibration, and accuracy.
Results—The model
included a medical history of depression or other psychiatric disorders,
hypertension, angina pectoris, and the
Barthel Index item dressing. The model had
acceptable discrimination, based on an area under the receiver operating
characteristic
curve of 0.78 (0.72–0.85), and calibration (P value of the U-statistic, 0.96). Transforming the model to an easy-to-use risk-assessment table, the lowest risk category (sum score, <−10)
showed a 2% risk of depression, which increased to 82% in the highest category (sum score, >21).
Conclusions—The clinical prediction model enables clinicians to estimate the degree of the depression risk for an individual patient within
the first week after stroke.